It has been a while since I have discussed Dr Tom SchneiderÃ¢â‚¬â„¢s EV computer simulation on the internet. I believe that Dr Schneider has properly modeled the mutation and selection process with his algorithm. I have used the principles that I have learned from his algorithm in my medical practice and found them very useful in treating infectious diseases.

It has been a while since I have discussed Dr Tom SchneiderÃ¢â‚¬â„¢s EV computer simulation on the internet. I believe that Dr Schneider has properly modeled the mutation and selection process with his algorithm. I have used the principles that I have learned from his algorithm in my medical practice and found them very useful in treating infectious diseases.

I was wondering if anyone is interested in this topic?

I believe there already was a thread on this started by this forums founder Fred Williams.

But anyway. I actually had a friendly discussion with a gal last year over a period of a couple weeks about this very simulation computer programming simulation idea. We were first discussing and debating the orign of a code (she had no answer), then the discussion morphed into modern day terms like bioinformatics - computational biology - etc. She told me her medical research company uses a bioinformatics computer simulation program called "Silico". She insisted that a computer simulation could be created to illustrate how a code developed in some type of "Abiogenesis" computer simulation. I told her that would be impossible since it has never been observed in the natural real world by any scientist and the only thing that would be demonstrated by such a computer simulation program would be the researching scientist's own wishful thinking and philosophy on how he thinks it got accomplished. For an accurate computer "Silico" program you need to experiment with something called "In vivo" (Latin for "within the living") or real world experimentation so as to find out the truth of the matter through proven empirical experimentation so as to design a computer Silico model to be able to accurately and truthfully teach others the reality of how it worked in the real world. Now with real world medical purposes, they can experiment and come up with a real world helpful model for the benefit of humans, but not so with evolution.

Otherwise all we have is a philosophical entertainment cartoon that has zero value as to truth or any practical applications. This is the same problem with Richard Dawkin's supposedly clever "Mutation Generator" which is nothing more than something rigged for a biased outcome modeled after his personal worldview. He's never proven anything in real life experimentation. The other challenge is how can you keep the computer programmers honest on this subject. The ultimate test for the computer programmers is to follow EXACTLY what was proven outside the computer room and that quite simply has not happened yet. For example here is a website that explains some of the most sacred of Evolutionist holy Icon beliefs which are pivitol to their church belief. Here's the site: Ten questions to ask your biology teacher about evolution.

So first off, you need to experiment and prove these popular stories/fables as an empirical FACT through real world experimentation and then create an exact model for the purpose of teaching others the truth as opposed to rigging a program to manipulate an audience with your religious unproven worldview. The problem with this Dr Schneider's and even Richard Dawkins' computer models is that the average person doesn't understand that they snuck in some hidden programmed information to reveal an outcome they wanted. If anything, that doesn't prove evolution, but rather intelligent design by someone's personal thoughts and ideas through manipulating the elements.

I had yet another discussion along these lines with the RNA World Hypothesis gang who insisted that they had done a real world experiment creating 20 amino acids proving through physics and chemicals the basis for "Abiogenesis". Unfortunately, like the Miller-Urey experiments there are things behind the scenes you don't here about. Miller doesn't tell you that after his electric Zap experiment with that electrical arc zapping, (simulating lightning or volcanoes as the spark of life) he had to rescue those 4 amino acids from the arc or they'd be destroyed. The modern experiment doesn't explain how they also had to do some last minute modiffication efforts to achieve results and all the while they are proving what they aim to refute and that is that it takes no intellignce to begat life. Here's a real experiment that would fall under the correct definitions of blind, pointless, pitiless indifference without purpose or intent evolution. set up an experiment with sterile sea water with all the trace elements we know of. Have an electrical arc set up to randomly Zap that sterile sea water and walk away. Check it from time to time over a period of millions of years and see whether or not codes develope, or if decoding mechanisms like Ribosomes just happen. That would be a real experiment by the evolutionary definitions games. Most modern science seems to be nothing more than one Internet/Lab experiment phenomena after another as opposed to the old way of actually getting out in the field and observing how nature actually does work.

They are out there pulling the same stunts with this Meteorite KT Extinction cartoon theory. The documentary programs on Discovery and National Geographic are loaded with this stuff and even Hollywood (another den of Atheism) eats this stuff up like candy for entertainment value with movies like Asteroids, Space Cowboys (remember the comet collision scenario?) and Deep Impact. Okay, it's fascinating and entertaining and we are always hearing about the latest news on near miss scenarios etc, but none of it is based on facts, just science-fiction cartoons. I have a personal interest in this extinction subject because I believe hugely in the Deluge extinction event which was largely caused by first a deep Earth hydrological event, which secondly triggered the rain event. But that's for another thread.

So that's my two bits on the computer simulation discussion. Until they actually do real world observation experiments and base the simulation on actual facts, then you're dealing with someone else's fantasy world the way they want you to see it through their eyes and this goes equally for so-called Creationist Scientists as well.

It has been a while since I have discussed Dr Tom SchneiderÃ¢â‚¬â„¢s EV computer simulation on the internet. I believe that Dr Schneider has properly modeled the mutation and selection process with his algorithm. I have used the principles that I have learned from his algorithm in my medical practice and found them very useful in treating infectious diseases.

I was wondering if anyone is interested in this topic?

Have you ever seen a beneficial genetic mutation in your medical career? They like to call any change a mutation if it's genetic or not or beneficial or not. The only verifiable list of mutations i've seen are genetic disorders that number in the hundreds and are'nt going to benefit any organism.

Have you ever seen a beneficial genetic mutation in your medical career? They like to call any change a mutation if it's genetic or not or beneficial or not. The only verifiable list of mutations i've seen are genetic disorders that number in the hundreds and are'nt going to benefit any organism.Enjoy.

Here are two of my favourite computer Silico simulations on how evolution works!!!

It has been a while since I have discussed Dr Tom SchneiderÃ¢â‚¬â„¢s EV computer simulation on the internet. I believe that Dr Schneider has properly modeled the mutation and selection process with his algorithm. I have used the principles that I have learned from his algorithm in my medical practice and found them very useful in treating infectious diseases.

It has been a while since I have discussed Dr Tom SchneiderÃ¢â‚¬â„¢s EV computer simulation on the internet. I believe that Dr Schneider has properly modeled the mutation and selection process with his algorithm. I have used the principles that I have learned from his algorithm in my medical practice and found them very useful in treating infectious diseases.

I was wondering if anyone is interested in this topic?

I believe there already was a thread on this started by this forums founder Fred Williams.

But anyway. I actually had a friendly discussion with a gal last year over a period of a couple weeks about this very simulation computer programming simulation idea. We were first discussing and debating the orign of a code (she had no answer), then the discussion morphed into modern day terms like bioinformatics - computational biology - etc. She told me her medical research company uses a bioinformatics computer simulation program called "Silico" . She insisted that a computer simulation could be created to illustrate how a code developed in some type of "Abiogenesis" computer simulation. I told her that would be impossible since it has never been observed in the natural real world by any scientist and the only thing that would be demonstrated by such a computer simulation program would be the researching scientist's own wishful thinking and philosophy on how he thinks it got accomplished. For an accurate computer "Silico" program you need to experiment with something called "In vivo" (Latin for "within the living") or real world experimentation so as to find out the truth of the matter through proven empirical experimentation so as to design a computer Silico model to be able to accurately and truthfully teach others the reality of how it worked in the real world. Now with real world medical purposes, they can experiment and come up with a real world helpful model for the benefit of humans, but not so with evolution.

Thank you Eocene for showing interest. It is true that Fred Williams has discussed this model and believes it has been discredited. However, I believe Fred is not quite correct about his analysis of this model. If you are willing to listen, I will show you how this model works and properly models the mutation and selection process. There is a reason why Tom Schneider does not publish all the data from his model.

Also, I am not an atheist, I am a creationist. When I registered, I indicated this but somehow the software is listing me as an atheist.

Otherwise all we have is a philosophical entertainment cartoon that has zero value as to truth or any practical applications. This is the same problem with Richard Dawkin's supposedly clever "Mutation Generator" which is nothing more than something rigged for a biased outcome modeled after his personal worldview. He's never proven anything in real life experimentation. The other challenge is how can you keep the computer programmers honest on this subject. The ultimate test for the computer programmers is to follow EXACTLY what was proven outside the computer room and that quite simply has not happened yet. For example here is a website that explains some of the most sacred of Evolutionist holy Icon beliefs which are pivitol to their church belief. Here's the site: Ten questions to ask your biology teacher about evolution.

I happen to believe that Richard DawkinsÃ¢â‚¬â„¢ Ã¢â‚¬Å“Mutation GeneratorÃ¢â‚¬Â is also correct. Have you ever asked yourself why his algorithm can sort and optimize for his simple sentence but can not sort and optimize for the and entire works of Shakespeare, let alone a single play? Once you understand Dr SchneiderÃ¢â‚¬â„¢s model, you will have the answer to this question.

So first off, you need to experiment and prove these popular stories/fables as an empirical FACT through real world experimentation and then create an exact model for the purpose of teaching others the truth as opposed to rigging a program to manipulate an audience with your religious unproven worldview. The problem with this Dr Schneider's and even Richard Dawkins' computer models is that the average person doesn't understand that they snuck in some hidden programmed information to reveal an outcome they wanted. If anything, that doesn't prove evolution, but rather intelligent design by someone's personal thoughts and ideas through manipulating the elements.

The experiments have been done numerous times. IÃ¢â‚¬â„¢ll show you the results, but it will take a little time. I think you will find it worth the effort.

I had yet another discussion along these lines with the RNA World Hypothesis gang who insisted that they had done a real world experiment creating 20 amino acids proving through physics and chemicals the basis for "Abiogenesis". Unfortunately, like the Miller-Urey experiments there are things behind the scenes you don't here about. Miller doesn't tell you that after his electric Zap experiment with that electrical arc zapping, (simulating lightning or volcanoes as the spark of life) he had to rescue those 4 amino acids from the arc or they'd be destroyed. The modern experiment doesn't explain how they also had to do some last minute modiffication efforts to achieve results and all the while they are proving what they aim to refute and that is that it takes no intellignce to begat life. Here's a real experiment that would fall under the correct definitions of blind, pointless, pitiless indifference without purpose or intent evolution. set up an experiment with sterile sea water with all the trace elements we know of. Have an electrical arc set up to randomly Zap that sterile sea water and walk away. Check it from time to time over a period of millions of years and see whether or not codes develope, or if decoding mechanisms like Ribosomes just happen. That would be a real experiment by the evolutionary definitions games. Most modern science seems to be nothing more than one Internet/Lab experiment phenomena after another as opposed to the old way of actually getting out in the field and observing how nature actually does work.

We can talk about abiogenesis some other time. First I want to show you how mutation and selection works.

They are out there pulling the same stunts with this Meteorite KT Extinction cartoon theory. The documentary programs on Discovery and National Geographic are loaded with this stuff and even Hollywood (another den of Atheism) eats this stuff up like candy for entertainment value with movies like Asteroids, Space Cowboys (remember the comet collision scenario?) and Deep Impact. Okay, it's fascinating and entertaining and we are always hearing about the latest news on near miss scenarios etc, but none of it is based on facts, just science-fiction cartoons. I have a personal interest in this extinction subject because I believe hugely in the Deluge extinction event which was largely caused by first a deep Earth hydrological event, which secondly triggered the rain event. But that's for another thread.

So that's my two bits on the computer simulation discussion. Until they actually do real world observation experiments and base the simulation on actual facts, then you're dealing with someone else's fantasy world the way they want you to see it through their eyes and this goes equally for so-called Creationist Scientists as well.

LetÃ¢â‚¬â„¢s see if I can throw a couple more pennies into the discussion.

It has been a while since I have discussed Dr Tom SchneiderÃ¢â‚¬â„¢s EV computer simulationÃ¢â‚¬Â¦

Have you ever seen a beneficial genetic mutation in your medical career? They like to call any change a mutation if it's genetic or not or beneficial or not. The only verifiable list of mutations i've seen are genetic disorders that number in the hundreds and are'nt going to benefit any organism.

Yes I have observed beneficial genetic mutations in my medical career. For example, IÃ¢â‚¬â„¢ve seen beneficial mutations in microbes that have bestowed resistance to drugs (in fact multiple drugs) and I have seen beneficial mutations in humans, for example sickle cell trait gives resistance to malaria. What Dr SchneiderÃ¢â‚¬â„¢s model shows is how you prevent these beneficial mutations in microbes from being fixed or amplified in a population and this solution is quite simple once you understand how mutation and selection works.

It has been a while since I have discussed Dr Tom SchneiderÃ¢â‚¬â„¢s EV computer simulationÃ¢â‚¬Â¦

Royal TrumanÃ¢â‚¬â„¢s paper did not quite hit the mathematical nail on the head. Ã¢â‚¬Å“Dr SchneiderÃ¢â‚¬â„¢s EV model, The Problem of Information for the Theory of Evolution. Has Tom Schneider Really Solved It?Ã¢â‚¬Â Dr Schneider did solve the problem; this is why he wonÃ¢â‚¬â„¢t publish all the data from his model. It shows that the theory of evolution is mathematically impossible; it also shows how to prevent drug resistance in microbes and prevent resistance in other mutating and selecting populations.

Since there seems to be some interest in understanding how Dr SchneiderÃ¢â‚¬â„¢s model works (and this in turn shows how mutation and selection works), I start with the basic principle of what his algorithm is. Dr SchneiderÃ¢â‚¬â„¢s algorithm is a sorting and optimization calculation. His algorithm sorts beneficial and detrimental mutations and optimizes the fitness to reproduce to the sorting (selection) conditions. Any questions on this starting point?

So far, no questions so I will continue my explanation of Dr SchneiderÃ¢â‚¬â„¢s EV algorithm and why it is an accurate simulation of the random mutation, natural selection sorting/optimization phenomenon.

The way one analyzes a computer simulation like Dr SchneiderÃ¢â‚¬â„¢s EV algorithm is by doing what is called a parametric study. Anyone interested in studying how mutation and selection works can access Dr SchneiderÃ¢â‚¬â„¢s algorithm on his web site at http://www-lmmb.ncif...java/index.html Note that you must have java available on your system to run his algorithm. I suggest if you do want to study his sorting/optimization algorithm, be systematic. This is not a trivial exercise since there are so many variables in this problem.

The gist of what a parametric study of a computer algorithm is to systematically vary each of the parameters of the model to map out the solution space. One could try to do this type of analysis with real systems. For example, one could try to analyze an example of mutation and selection in a microbiology laboratory but controlling each of the variables such as population size, mutation rate, genome size, selection conditions and so on would me a massive, virtually impossible undertaking. The cost for each data point would be extraordinary and the total cost for such a study would be astronomical. This is why algorithms like Dr SchneiderÃ¢â‚¬â„¢s EV model are so important. One can study the behavior of such a process on your home computer. One can then compare real examples of mutation and selection and see whether the behavior of the model matches in any way the behavior of the model. I have done this with Dr SchneiderÃ¢â‚¬â„¢s model and his model does properly describe how mutation and selection works.

If you want to get a start on this parametric study, try lengthening the genome length in Dr SchneiderÃ¢â‚¬â„¢s model. What you will see is the generations required to sort and optimize the selection conditions rapidly goes up until the algorithm ceases to sort and optimize the selection conditions. Why do you think this happens?

Once you understand how mutation and selection works, you can use these principles to interfere with the mutation and selection process. You will also understand why mutation and selection can not make the massive genetic transformations required for the theory of evolution to be plausible.

Yes I have observed beneficial genetic mutations in my medical career. For example, IÃ¢â‚¬â„¢ve seen beneficial mutations in microbes that have bestowed resistance to drugs (in fact multiple drugs) and I have seen beneficial mutations in humans, for example sickle cell trait gives resistance to malaria.

Drug resistance and even nylon eating bacteria are adaptations by amino acid replacement or an entire protein being substituted by another. The gene already has the ability to produce different proteins and the genes themselves show no evidence of mutation.

Sickle cell may be a genetic mutation, but it is beneficial at a cost since it deforms the red blood cells making them less efficient at transporting oxygen. If Dr SchneiderÃ¢â‚¬â„¢s EV algorithm is valid in reality, then we would expect a verifiable list of beneficial genetic mutations that outnumber the deleterious list in Post #3 by a ratio of 100:1. Given the fact that the empirical evidence is the opposite of what is predicted, it also casts doubt on the ability of natural selection to weed out deleterious mutations.

Yes I have observed beneficial genetic mutations in my medical career. For example, IÃ¢â‚¬â„¢ve seen beneficial mutations in microbes that have bestowed resistance to drugs (in fact multiple drugs) and I have seen beneficial mutations in humans, for example sickle cell trait gives resistance to malaria.

Drug resistance and even nylon eating bacteria are adaptations by amino acid replacement or an entire protein being substituted by another. The gene already has the ability to produce different proteins and the genes themselves show no evidence of mutation.

Hi jason777, how do you propose amino acid replacements occur? The following quote is from your source.

Here, we propose that amino acid replacements in the catalytic cleft of a preexisting esterase with the β-lactamase fold resulted in the evolution of the nylon oligomer hydrolase.

What is the mechanism that got these amino acids into the catalytic cleft? I suppose there could be errors in DNA transcription that would give different proteins from a given gene, however that error would have to be repeated over and over in order to obtain a significant amount of the protein. DNA transcription has to occur with high fidelity otherwise you would have any number of proteins being produced. It doesnÃ¢â‚¬â„¢t sound like a very efficient biological system to me.

Sickle cell may be a genetic mutation, but it is beneficial at a cost since it deforms the red blood cells making them less efficient at transporting oxygen. If Dr SchneiderÃ¢â‚¬â„¢s EV algorithm is valid in reality, then we would expect a verifiable list of beneficial genetic mutations that outnumber the deleterious list in Post #3 by a ratio of 100:1. Given the fact that the empirical evidence is the opposite of what is predicted, it also casts doubt on the ability of natural selection to weed out deleterious mutations.

I donÃ¢â‚¬â„¢t recall saying that Sickle cell trait gave the best functioning form of hemoglobin. In fact, the reason Sickle cell trait gives selective advantage to those with the trait in a malaria endemic area is that the malaria parasite can not complete itsÃ¢â‚¬â„¢ life cycle in this partially crippled form of hemoglobin. It is not unusual for a mutation to give a less efficient form of a protein but in an environment with particular selection pressures, this less efficient may confer selection advantage. Remove the selection pressures which give benefit to those members of the population then over time these less efficient variants will reduce in frequency in the population.

Dr SchneiderÃ¢â‚¬â„¢s sorting/optimization algorithm gives the best possible situation for a population. The best half of the population can always reproduce, there is no extinction. Dr SchneiderÃ¢â‚¬â„¢s algorithm can sort and optimize within limits. The question that should be answered is why does his algorithm stop sorting and optimizing under certain circumstances? Answer that question and you will understand how mutation and selection works.

Jason777 and others reading this thread, do selection pressures increase the diversity of a population or reduce the diversity of a population? Dr SchneiderÃ¢â‚¬â„¢s algorithm answers this question as well.

What is the mechanism that got these amino acids into the catalytic cleft?

Unknown. We only observed the "before and after" not the actual process. Although, no mutation was observed in the genes or the DNA, which makes protein substitution the most likely mechanism. All genes are able to produce more than one protein. A gene in fruitflies is able to transcript more than 30,000, which makes adaptation a function and not a mutation.

Sickle cell may be a genetic mutation, but it is beneficial at a cost since it deforms the red blood cells making them less efficient at transporting oxygen. If Dr SchneiderÃ¢â‚¬â„¢s EV algorithm is valid in reality, then we would expect a verifiable list of beneficial genetic mutations that outnumber the deleterious list in Post #3 by a ratio of 100:1. Given the fact that the empirical evidence is the opposite of what is predicted, it also casts doubt on the ability of natural selection to weed out deleterious mutations.

Dr SchneiderÃ¢â‚¬â„¢s sorting/optimization algorithm gives the best possible situation for a population. The best half of the population can always reproduce,there is no extinction. Dr SchneiderÃ¢â‚¬â„¢s algorithm can sort and optimize within limits. The question that should be answered is why does his algorithm stop sorting and optimizing under certain circumstances? Answer that question and you will understand how mutation and selection works.

I'm not saying his simulation does'nt work. I'm saying it contradicts reality. I posted a huge list of known deleterious mutations that are accumulating in the human population, but there are only a few known mutations that may be beneficial in a given circumstance.

What is the mechanism that got these amino acids into the catalytic cleft?

Unknown. We only observed the "before and after" not the actual process. Although, no mutation was observed in the genes or the DNA, which makes protein substitution the most likely mechanism. All genes are able to produce more than one protein. A gene in fruitflies is able to transcript more than 30,000, which makes adaptation a function and not a mutation.

Well jason777, this is certainly new for me. When I studied biochemistry, the basic mechanism of formation of proteins was the DNA gives the code, three bases define a codon, and one codon defines which amino acid is transcribed from the DNA. There is a vast amount of data that verifies this concept. The entire field of genetic engineering of synthetic proteins is based on these principles. Are you familiar with the one gene one polypeptide theory?

This is a bit of a side track on the topic of this thread but if you have a citation for your fruitfly example, IÃ¢â‚¬â„¢ll read it and give you my comment.

Sickle cell may be a genetic mutation, but it is beneficial at a cost since it deforms the red blood cells making them less efficient at transporting oxygen. If Dr SchneiderÃ¢â‚¬â„¢s EV algorithm is valid in reality, then we would expect a verifiable list of beneficial genetic mutations that outnumber the deleterious list in Post #3 by a ratio of 100:1. Given the fact that the empirical evidence is the opposite of what is predicted, it also casts doubt on the ability of natural selection to weed out deleterious mutations.

Dr SchneiderÃ¢â‚¬â„¢s sorting/optimization algorithm gives the best possible situation for a population. The best half of the population can always reproduce, there is no extinction. Dr SchneiderÃ¢â‚¬â„¢s algorithm can sort and optimize within limits. The question that should be answered is why does his algorithm stop sorting and optimizing under certain circumstances? Answer that question and you will understand how mutation and selection works.

I'm not saying his simulation does'nt work. I'm saying it contradicts reality. I posted a huge list of known deleterious mutations that are accumulating in the human population, but there are only a few known mutations that may be beneficial in a given circumstance.

There may be many deleterious mutations in the human population but you need to understand something about natural selection. A female with cystic fibrosis or sickle cell disease will not be as healthy as a female without these diseases. Therefore she will have a more difficult time passing her genetic information to further generations. These deleterious mutations will not amplify over generations. Only beneficial mutations which give more fit members of the population will tend to amplify over generations. This is a fundamental principle you need to understand about selection, whether you are talking about recombination and natural selection or random mutation and natural selection. The more fit members of a population who are able to put more energy into reproduction are better able to pass their genetic information to further generations.

Not only does Dr SchneiderÃ¢â‚¬â„¢s EV algorithm work under some circumstances, it properly reflects reality. The key to understanding how it reflects reality is understanding why Dr SchneiderÃ¢â‚¬â„¢s algorithm stops working when certain parameters are used, such as long genome lengths.

Jason777, you didnÃ¢â‚¬â„¢t try to answer my question about whether selection increases or decreases the diversity of a population. I ask this question of many biology students and occasionally their instructors and they usually get it wrong. In fact, when National Geographic honored DarwinÃ¢â‚¬â„¢s 200th anniversary, they got it wrong when the attributed selection for causing the diversity of populations. Selection kills or impairs the reproduction of the weaker members of a population. This is what I do to microbial populations when I use antimicrobial agents. In principle, I am trying to drive these populations to extinction, the end point to extreme selection pressure on a population.

If you want to increase the diversity of a population, remove selection pressures. A good example can be seen in the contrast between rainforests and deserts. The former has uniform temperatures and abundance of food and water. You get a wide variety of life forms in this low selection pressure environment. On the other hand, deserts have temperature extremes, dehydration and paucity of food. Only very hearty life forms can survive in this environment. Disturb a rainforest and you easily harm this fragile environment. Build a house in the desert and the rattlesnakes are happy to move into your basement.

So jason777, do you understand why longer genome lengths cause Dr SchneiderÃ¢â‚¬â„¢s EV algorithm to slow and ultimately stop sorting and optimizing? This is the key to understanding how his algorithm reflects reality. Once you understand this, IÃ¢â‚¬â„¢ll start presenting you with empirical examples that behave the same way.

As another example how Dr SchneiderÃ¢â‚¬â„¢s model properly models properly captures elements of the mutation and selection process, consider the following figure from his web site:This graph was produced using his 256 base published case. Note that when his calculation is initiated his Ã¢â‚¬Å“creaturesÃ¢â‚¬Â have random sequences of bases. How more diverse can you get than random. Selection is initiated and the random sequences are sorted and optimized to match the selection conditions. The diversity of the population is reduced by the selection conditions until virtually the entire population has the same genetic sequences. At generation 1000, selection is turned off (the red line) and the genetic sequences revert to random sequences over time (the greatest possible diversity).

If any of you have studied quantum thermodynamics, you might recognize the mutation without selection (Ã¢â‚¬Å“no selectionÃ¢â‚¬Â) portion of the curve as an example of the 2nd law of thermodynamics. Selection increases order and reduces diversity while mutations without selection reduces order and ultimately gives random sequences and increases diversity.

Well jason777, this is certainly new for me. When I studied biochemistry, the basic mechanism of formation of proteins was the DNA gives the code, three bases define a codon, and one codon defines which amino acid is transcribed from the DNA. There is a vast amount of data that verifies this concept. The entire field of genetic engineering of synthetic proteins is based on these principles. Are you familiar with the one gene one polypeptide theory?

This is a bit of a side track on the topic of this thread but if you have a citation for your fruitfly example, IÃ¢â‚¬â„¢ll read it and give you my comment.

The surprising news released by scientists this week that it takes only 30,000 genes -- one-third what was widely predicted -- to make a human has biologists scratching their heads trying to explain how so few genes can make people tick.

The answer may be found in the sprawling network of proteins produced by human cells at the direction of the genes. Some researchers are now theorizing that the human genome, the term for the complete set of genes, may provide cells with instructions to make as many as 300,000 different proteins.

Over the last few years, scientists at major pharmaceutical companies and a slew of smaller biotech firms have turned their attention to such proteins, racing one another to discover new ones and determine how they work. That's because proteins likely hold the ultimate key to new disease-diagnostic tests and novel drugs.

The human genome isn't "a blueprint but a building-materials list" for proteins, says Richard Caprioli, a biochemist at Vanderbilt University. "We know what components go into a cell, but what do they do? There is a host of interactions, and it's the interactions that make a cell healthy or unhealthy."

Cells produce proteins by reading information inside genes. So discovering genes provides scientists with critical clues about proteins. Proteins, in turn, act as the building blocks of life, the chemical messengers between cells and other proteins, and the freight carriers that transport other proteins around the body, often via the bloodstream. By understanding proteins, scientists believe they can finally solve the basic biochemical mechanisms underlying sickness and health.

"While we have the sequence of the entire genome, we don't know the identity of all the genes and the proteins they encode. It's the understanding of these proteins that will lead to breakthrough drugs," says Jonathan Rothberg, chairman and chief executive of CuraGen Corp. of New Haven, a pioneer in mapping proteins and their functions. CuraGen is one of a host of companies that have sprouted in the last few years to use a technique called "proteomics" to catalog and comprehend the actions of proteins.

Besides satisfying the scientific community, detailed knowledge about proteins has become critical for protecting biological discoveries for commercial gain. "If you know what a gene's protein does you can get a patent for the gene," Mr. Rothberg says. "If you don't know what it does, you get sent home." So far, researchers at CuraGen have secured more than 500 patents on 1,300 genes and the proteins they encode. Already, he says, scores of these discoveries are being tested for the development of new drugs by CuraGen and its research collaborators, including the drug giants Bayer AG, GlaxoSmithKline PLC and Roche Holding Ltd.

Until recently, scientific dogma said that each gene makes only one protein. But if that were true, the human machine would only have 30,000 parts, one for each gene. That notion, scientists say, is impossible to believe because so few proteins wouldn't be able to account for the complexity of Homo sapiens. Scientists are reluctant to guess how many proteins might be at work inside humans, especially after the research community so badly missed the true number of genes. But it seems clear that there are likely to be several hundred thousand proteins, once a census that may take decades to complete is done.

The precise number of proteins one gene can create was one of the key unresolved questions raised Monday at a press conference in Washington, D.C. There, Celera Genomics Group and the public Human Genome Project each unveiled their separate maps of the human genome. Having decoded the sequence of the three billion chemical units that make up human DNA, the two groups separately were able to determine that humans comprise about 30,000 genes -- only twice as many as in a fruit fly. But the researchers also agreed that knowing the sequence of letters that makes up a gene provides only the initial clues for cracking the mystery of proteins.

"We're pretty complex," says Paula Grabowski, a biologist at the University of Pittsburgh who specializes in how genes are regulated. She says an important biochemical mechanism helps explain why there are so many more proteins than genes: Various parts of a gene often get reshuffled like a deck of cards; each ordering of the cards can produce a different protein.

Dr. Grabowski says that recent research estimated that a particularly prolific gene in the fruit fly may produce as many as 98,000 different proteins. Even if that calculation turns out to be on the high side, she says, it is clear that some genes can produce prodigious numbers of proteins. In humans, she says, there are already examples of genes that are "alternatively spliced," as scientists describe the process, to make many related proteins used in chemical communications in the brain.

Well jason777, this is certainly new for me. When I studied biochemistry, the basic mechanism of formation of proteins was the DNA gives the code, three bases define a codon, and one codon defines which amino acid is transcribed from the DNA. There is a vast amount of data that verifies this concept. The entire field of genetic engineering of synthetic proteins is based on these principles. Are you familiar with the one gene one polypeptide theory?

This is a bit of a side track on the topic of this thread but if you have a citation for your fruitfly example, IÃ¢â‚¬â„¢ll read it and give you my comment.

"We're pretty complex," says Paula Grabowski, a biologist at the University of Pittsburgh who specializes in how genes are regulated. She says an important biochemical mechanism helps explain why there are so many more proteins than genes: Various parts of a gene often get reshuffled like a deck of cards; each ordering of the cards can produce a different protein.

Dr. Grabowski says that recent research estimated that a particularly prolific gene in the fruit fly may produce as many as 98,000 different proteins. Even if that calculation turns out to be on the high side, she says, it is clear that some genes can produce prodigious numbers of proteins. In humans, she says, there are already examples of genes that are "alternatively spliced," as scientists describe the process, to make many related proteins used in chemical communications in the brain.

Jason777, are you claiming that genes randomly reshuffle to make new proteins? If so, how do these new proteins incorporate into complex chemical sequences such as the Krebs cycle or blood coagulation? Are you claiming that this is how bacteria become resistant to antibiotics?

Transposition and recombination of pieces DNA has been long known. This is how we produce antibodies to wide varieties of antigens, but this does not occur in germ cell lines. This is why even though you may be immunized for measles and have antibodies against this disease; your children still need to be vaccinated. Certainly we produce thousands of proteins that were not initially coded directly in the DNA we got from our parents, immunoglobins are a long known example, there may be other proteins that are formed this way but I doubt it is a random process and I doubt this is the way bacteria evolve resistance. Even if this is the way bacteria evolve resistance, how do bacteria shuffle and transpose DNA for two selection pressures simultaneously?

Jason777, you didnÃ¢â‚¬â„¢t try to answer my question about whether selection increases or decreases the diversity of a population.

Sorry, i'm not concerned with hypothetical models. I look at the number of known deleterious mutations and compare it to the known beneficial mutations and the math makes it empirical instead.

LetÃ¢â‚¬â„¢s see if I can take Dr SchneiderÃ¢â‚¬â„¢s model out of the hypothetical and relate it to the empirical evidence.

Since no one seems to be interested in running Dr SchneiderÃ¢â‚¬â„¢s algorithm with longer genome lengths than his published example, IÃ¢â‚¬â„¢ll tell you what happens. The algorithm becomes increasing slow until it ultimately stops sorting and optimizing. There are several reasons for this. First, the search space for the optimization process goes up by 4^G where G is the genome length. Then, the fitness landscape becomes more and more complex. The algorithm will always try to find an optimum but in a complex search space that optimum may only be a local optimum, not a global optimum. If the starting point for the genetic sequences in his algorithm can not find a trajectory that gives increasing fitness each step of the evolutionary process, the population will stop evolving once it has reached the local optimum. This is similar to what happens with biologic populations. One could construct the fitness landscape for Dr SchneiderÃ¢â‚¬â„¢s algorithm. This would require computing the fitness for each of the 4^G possible sequences of bases. For Dr SchneiderÃ¢â‚¬â„¢s published example that would be 4^256 points on the fitness landscape. Even Dr SchneiderÃ¢â‚¬â„¢s trivially small genome published example gives a huge number of points on the fitness landscape. Consider what the fitness landscape would look like for a 500,000 base genome (about the size of the smallest self replicating genome known) or 3,000,000,000 bases (about the size of the human genome). Even viral genomes like HIV have 17,000 bases giving a vastly larger fitness landscape then Dr SchneiderÃ¢â‚¬â„¢s trivially small genome example.

Therefore, when one tries to make Dr SchneiderÃ¢â‚¬â„¢s sorting/optimization algorithm operate in too large a search space, the process comes to a standstill. How could you change the input parameters so that Dr SchneiderÃ¢â‚¬â„¢s model will converge, even with a large genome? The way to do this with Dr SchneiderÃ¢â‚¬â„¢s model is to reduce the complexity of the selection conditions. Set any two of the three selection conditions to zero. Dr SchneiderÃ¢â‚¬â„¢s algorithm will sort and optimize the remaining selection condition very rapidly, even on long genomes.

In microbiology the roles of mutation and selection in evolution are coming to be better understood through the use of bacterial cultures of mutant strains. In more immediately practical ways, mutation has proven of primary importance in the improvement of yields of important antibiotics - such as in the classic example of penicillin, the yield of which has gone up from around 40 units per ml of culture shortly after its discovery by Fleming to approximately 4,000, as the result of a long series of successive experimentally produced mutational steps. On the other side of the coin, the mutational origin of antibiotic-resistant micro-organisms is of definite medical significance. The therapeutic use of massive doses of antibiotics to reduce the numbers of bacteria which by mutation could develop resistance, is a direct consequence of the application of genetic concepts. Similarly, so is the increasing use of combined antibiotic therapy, resistance to both of which would require the simultaneous mutation of two independent characters.

As an important example of the application of these same concepts of microbial genetics to mammalian cells, we may cite the probable mutational origin of resistance to chemotherapeutic agents in leukemic cells 44, and the increasing and effective simultaneous use of two or more chemotherapeutic agents in the treatment of this disease.

I added the highlighting.

Let me expand on a particular sentence from this quote, Ã¢â‚¬Å“Similarly, so is the increasing use of combined antibiotic therapy, resistance to both of which would require the simultaneous mutation of two independent characters.Ã¢â‚¬Â

LetÃ¢â‚¬â„¢s say that I am treating an infection with antibiotic A. And letÃ¢â‚¬â„¢s say that in order for the bacterial population to evolve resistance to antibiotic A that it requires only a single mutation. And letÃ¢â‚¬â„¢s say that the probability of that mutation occurring is 1 in a million. Now letÃ¢â‚¬â„¢s say there are a billion bacteria. In the next generation there will be a thousand members with resistance to antibiotic A. Now letÃ¢â‚¬â„¢s say that I use a second antibiotic B which targets a different gene. And letÃ¢â‚¬â„¢s say that the population only requires a single mutation (at a different locus than antibiotic A) to evolve resistance to antibiotic B but in this case, I use antibiotic A and B together. The probability that any descendent have both beneficial mutations simultaneously is 1 in a million times 1 in a million equals 1 in a trillion. This is what Edward Tatum is talking about Ã¢â‚¬Å“the simultaneous mutation of two independent characters.Ã¢â‚¬Â

I use this principle in my medical practice when treating infections, especially with multi-drug resistant strains of bacteria. Increasing the selection pressure on a population does not accelerate the mutation and selection process, it suppresses the process and is much more likely to drive the population to extinction. You are not trained this way in medical school, in fact you are advised not to use combination therapy.

This is the simplest example of how to suppress the mutation and selection process. Imposing more stringent sorting conditions in Dr SchneiderÃ¢â‚¬â„¢s algorithm impairs his algorithm from evolving the selection conditions. Biological populations are no better at solving this problem. In my next post, IÃ¢â‚¬â„¢ll explain how populations solve more complex mutation and selection problems and why the failure of biologists (evolutionists since they control the field of biology) to properly elucidate this process has led to multi-drug resistant bacteria.

The following paper describes a particularly important example of mutation and selection. Unlike the case describe in my previous post where only a single mutation is required to give resistance, the following citation describes a case where multiple mutations are required in order to get strong resistance to Beta-lactam (Penicillin family) drugs.Ã¢â‚¬Å“Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter ProteinsÃ¢â‚¬ÂDaniel M Weinreich, Nigel F Delaney, Mark A DePristo, Daniel L Hartl. Science. Washington: Apr 7, 2006. Vol. 312, Iss. 5770; pg. 111

Five point mutations in a particular Beta-lactamase allele jointly increase bacterial resistance to a clinically important antibiotic by a factor of ~100,000. In principle, evolution to this high-resistance Beta-lactamase might follow any of the 120 mutational trajectories linking these alleles. However, we demonstrate that 102 trajectories are inaccessible to Darwinian selection and that many of the remaining trajectories have negligible probabilities of realization, because four of these five mutations fail to increase drug resistance in some combinations. Pervasive biophysical pleiotropy within the Beta-lactamase seems to be responsible, and because such pleiotropy appears to be a general property of missense mutations, we conclude that much protein evolution will be similarly constrained. This implies that the protein tape of life may be largely reproducible and even predictable.

The probability that any one descendent gets all 5 mutations simultaneously is the product of each of the individual probabilities, an extremely tiny likelihood. The way populations solve these probability problems is by finding a trajectory on the fitness landscape where a sequence of mutations each giving improved fitness over the previous allele takes the population to a new optimum. Each mutation in the sequenced is amplified or fixed in the population so that the next mutation in the sequence has a reasonable probability of occurring at the proper locus.

In other words, the first beneficial mutation in the sequence occurs at the proper locus giving an allele with better reproductive fitness against the selection pressure. Then over several hundred or thousand generations, the beneficial allele is amplified or fixed in the population. Now you have millions of members of the population with the beneficial allele with the first mutation in the sequence. Then the probability for the second beneficial mutation to occur at the proper locus has a reasonable probability of occurring to some member of the population with the first beneficial mutation. This allele with the first two beneficial mutations in the sequence gives a new allele with better reproductive fitness than the allele with only the first mutation. Then again over several hundred or thousand generations more, this allele with the first two mutations in the sequence is amplified or fixed in the population setting the stage for the third mutation in the sequence. This process is repeated for the third, then the fourth and finally the fifth mutation giving a highly resistant population to beta-lactam drugs.

The multiplication rule does not apply to biological evolution. A common error in the non-scientific literature and poorly written papers is to assume that probabilities multiply for computing components of living things such as proteins. A typical argument notes that proteins are about 300 amino acids long and that there are 20 different kinds of amino acids. If such a string were to be generated using independent selection of the amino acids, then the probability of generating any particular string is 20^-300, a very small number indeed. While this may be true for random strings, it does not directly apply to proteins found in living organisms. Why? Because individual mutations accumulate one-at-a-time and there is amplification (replication) between steps.

The point Dr Schneider misses is that populations can not amplify multiple alleles simultaneously. Dr SchneiderÃ¢â‚¬â„¢s own EV algorithm demonstrates that combining selection pressures interferes with this process. This is one of the reasons his algorithm can sort and optimize only trivially tiny genomes. If the population is exposed to a second antibiotic which targets a different gene while being exposed to the beta-lactam drug, the amplification process will be impaired. Even if the first beneficial mutation in the sequence for beta-lactam resistance occurs, the second antibiotic will interfere with the ability of the population to fix the first beneficial mutation. It is extremely difficult if not impossible for a population to amplify two alleles simultaneously.

Neither Dr Schneider in his The AND-Multiplication Error nor Fred Williams in his thread Tom SchniederÃ¢â‚¬â„¢s Ã¢â‚¬Ëœthe And-multiplication ErrorÃ¢â‚¬â„¢ Article Refuted got this point quite right. A complete analysis of Dr SchneiderÃ¢â‚¬â„¢s EV program shows that his algorithm properly simulates reality and shows how the mutation and selection process works. Amplification does occur but only under limited circumstances. Once you understand how the mutation and selection, sorting/optimization/amplification process works, it is easy to interfere with and suppress this process.

JBS Haldane in his classic paper The Cost of Natural Selection where he estimates the number of generations required to fix a beneficial allele made the following comment:

Can this slowness be avoided by selecting several genes at a time? I doubt it, for the following reason. Consider clonally reproducing bacteria, in which a number of disadvantageous genes are present, kept in being by mutation, each with frequencies of the order of 10^-4. They become slightly advantageous through a change of environment or residual genotype. Among 10^12 bacteria there might be one which possessed three such mutants. But since the cost of selection is proportional to the negative logarithm of the initial frequency the mean cost of selecting its descendants would be the same as the selection for the three mutants in series, though the process might be quicker.

If you plot HaldaneÃ¢â‚¬â„¢s curves for the cost of substitution (amplification) of a beneficial allele, you will find that at very low initial frequency of the beneficial allele, the cost of substitution goes to infinity, (negative logarithms approach infinity as the value approaches zero).

The trajectories available to a population on a fitness landscape are limited, even for single targeted selection pressures such as with the Penicillin type drugs. Forcing a population to take two trajectories simultaneously is mathematically and empirically far more difficult. A third selection pressure makes the mutation and selection process virtually impossible. In my next post, I will give empirical examples of this case.

I canÃ¢â‚¬â„¢t tell whether I am confusing you or boring you but IÃ¢â‚¬â„¢ll try once again to show you empirical examples of how Dr SchneiderÃ¢â‚¬â„¢s EV algorithm properly simulates mutation and selection. I hope I have sufficiently demonstrated that Dr SchneiderÃ¢â‚¬â„¢s program shows that the larger the search space for sorting and optimizing, the more difficult it is to do this mathematically. This is the same problem which confronts biological populations trying to adapt to selection pressures by mutation and selection. One of the arguments about the validity of Dr SchneiderÃ¢â‚¬â„¢s algorithm is that it does not allow extinction. However, there are examples in the biological world of mutation and selection problems which do not allow extinction. In fact, there are important medical problems which have to address this difficulty. The most common of these examples occurs in one of the fastest mutating, highest replication diseases physicians have to deal with. This example is the human immunodeficiency virus better known as HIV. Suppression of the mutation and selection process of this virus requires three simultaneous targeted selection pressures. Here are some citations which demonstrate this effect.

In 1994, Dr. David Ho discovered that what was then thought of as a latency phase -- when a person was infected with HIV but not experiencing any symptoms -- was in fact a period of continuous onslaught, in which the virus and the immune system are engaged in a pitched battle. Once he was able to measure the amount of virus in the blood, he learned that in fact billions of HIV particles were being produced every day. This breakthrough allowed Ho and his collaborators to come up with the idea for combination therapy -- treating a person with several drugs at once to suppress the virus down to undetectable levels. Patients near death rebounded dramatically after beginning what was called "triple cocktail" therapy, and Ho was named Time magazine's "Man of the Year" in 1996 for his work. In this wide-ranging interview, Ho recounts his breakthrough discoveries and his battles against the virus over the years. He also talks about the implications of combination therapy on the future of the epidemic and the importance of prevention efforts. "We have to bear in mind that during the years where this concerted treatment effort took place, approximately 2 million were treated. But during those years, another 15 million or so got newly infected." Currently Ho is executive director of the Aaron Diamond AIDS Research Center, where he is working on potential vaccine approaches, which he also discusses here. This transcript is drawn from four interviews conducted in New York and China in April and June 2005, and March 2006.

and

The consequence of that obviously is central to thinking about how HIV destroys the immune system, but also it has great ramifications for therapy, because HIV is an error-prone virus. As it replicates it makes mistakes. Now, that may not be all bad, because mistakes allow HIV to generate new variants, some of which will allow it to survive in the presence of drugs, survive in the presence of immune attack, so that's actually an advantage to HIV. When we know how much virus replication is going on and we know the error rate with which the virus makes mistakes, then we could begin to calculate what HIV would do if we applied drug pressure, and from those type of calculations came to the conclusion that it's inevitable for HIV to develop drug resistance if you give it one drug at a time. However, if you start to combine the drugs and try to force the virus into a corner using multiple drugs, it is exceedingly difficult or statistically improbable for HIV to become resistant to all the drugs simultaneously. That for us formed the foundation of thinking about combination therapy.

David Ho was made Time Magazine Ã¢â‚¬Å“Man of the YearÃ¢â‚¬Â for rediscovering what Edward Tatum said more than thirty years earlier.

Combinations of antiretrovirals create multiple obstacles to HIV replication to keep the number of offspring low and reduce the possibility of a superior mutation. If a mutation arises that conveys resistance to one of the drugs being taken, the other drugs continue to suppress reproduction of that mutation. With rare exceptions, no individual antiretroviral drug has been demonstrated to suppress an HIV infection for long; these agents must be taken in combinations in order to have a lasting effect. As a result the standard of care is to use combinations of antiretroviral drugs. Combinations usually comprise two nucleoside-analogue RTIs and one non-nucleoside-analogue RTI or protease inhibitor.[2]

The combined antiviral effects of indinavir and RT inhibitors dramatically suppressed the emergence of resistance to these agents, in a bi-directional fashion, relative to the rates observed during inhibition of the protease or RT alone. This suggests that the durability of viral inhibition can be increased by combining indinavir with one or more inhibitors of the reverse transcriptase, suppressing the viral replication that drives the evolution of resistance.

Monotherapy does not give long-term suppression of viral replication and evolution, and combination therapy is viewed as a potentially more effective long-term approach based on increased and more durable suppression of HIV replication.

Modern treatment of HIV uses three drugs which target three loci. These three selection pressures effectively stymie the mutation and selection process. Dr SchneiderÃ¢â‚¬â„¢s EV algorithm shows that the more complex you make the sorting and optimization conditions, the much, much slower the process is until it is finally suppressed. The same effect is observed in biological systems such as HIV. Note that individuals with HIV do not get extinction of the virus. There is always a residual population however the population is small enough that usually the clinical signs of disease are suppressed.

Mutation and selection is a mathematically predictable process as demonstrated by Dr SchneiderÃ¢â‚¬â„¢s EV algorithm. The behavior of his mathematical algorithm is analogous to biological examples. His algorithm also shows why the theory of evolution is mathematically impossible. The mutation and selection process is far to slow and the selection conditions do not exist to carry out such transformations. In addition, mutation and selection could not occur in the so called primordial soup because there was no replicator. One of the assumptions Dr Schneider made in the formulation of his algorithm was that random sequences could initially replicate. In order for amplification to occur, a replicator is needed. There is no evidence either mathematically or empirically that such a replicator existed.

It requires careful analysis and evaluation of the assumptions used when appraising any computer simulation. I may be the only creationist that believes Dr SchneiderÃ¢â‚¬â„¢s model is a credible tool for evaluating sorting/optimization problems (which mutation and selection represents an example). This is not good when other creationists who do not understand fully what his model shows discard its results out of hand. Dr Schneider has not abandoned his model but refuses to publish all the data from his model and explain its behavior. Dr Schneider has retreated to the position that his model shows information gain which technically it does. What Dr Schneider seems to have forgotten is that he works for the National Cancer Institute, an institution dedicated to finding cures and treatment for cancers. Cancers are mutating and selection populations. Dr Schneider does harm to the patients he is paid to help by not fully describing the results of his model. If anyone doubts the veracity of this statement, I can post the dozens of citations I have found which show that combination therapy for cancer gives better, more durable treatment. Edward Tatum already recognized this in his 1958 Nobel laureate lecture. Sorting/optimization problems whether they are mathematical or biological as seen with mutation and selection are suppressed when anything more than simple sorting conditions are used. This is why the theory of evolution is mathematically impossible and this is how one uses the understanding of how mutation and selection works to address this problem when fighting disease subject to this phenomenon. Apparently the theory of evolution is more important to Dr Schneider than properly explaining the results of his model. Despite the fact that he works for a government institution committed to fighting diseases subject to the mutation and selection phenomenon.

I do wonder what the evolutionists will say this time--if they actually respond to this instead of ignoring it.

Thanks Cata, and thanks to the Lord.

What you are observing is one of the greatest blunders of evolutionism which is the failure to properly describe the basic science and mathematics of mutation and selection. It is almost impossible to understand this phenomenon without using mathematics considering all the variables involved.

James Crow of the University of Wisconsin published this opinion piece Mayr, mathematics and the study of evolution where he believes that there will be an increase in the use of mathematics to study this process. He has had virtually no response to his piece. This is why I believe Dr SchneiderÃ¢â‚¬â„¢s algorithm is so important. Mathematics gives the tools to understand this process and clear the air of all the fallacies that evolutionist indoctrination has instilled in so many people. The theory of evolution can not stand up to hard mathematical science.

Thanks for the encouragement Cata. Perhaps if you understand some of what I am saying, you will be able to do something about it. Sorry for all the problems our generation is dumping on you young people but trust in the Lord.

Thanks for joining the site. As you know bacteria have plasmids-- offhand the famous F-plasmid is an example of fully coded genes which when passed encode a s@x pilus in the former female bacteria. It is hard to conceive that the DNA evolved to do this. Theoretically, selection would have done away with a nonfunctional partially evolved pilus. Unless you can at every step prove a useful function. But then there are the accompanying enzymes and signal proteins that must be coded for simultaneously in order for the DNA to be useful--a very complicated process of which (as you know) we don't have full understanding.

I said all that to say this: Bacteria, I believe, were designed to function as the decomposers of things. There are thousands new species bacteria being found throughout the ocean. It seems perhaps they have the ability to speciate, and have great adaptability, but they are still bacteria.

Are you advocating macroevolution? If so, how can you be a creationist? Are you a theistic evolutionist?

P.S. I only read the first few posts at first. I see you are not advocating evolution at all. I'm not strong in math and physics, so when the math thing comes into microbiology, I kind of shy away--any suggestions or encouragement? Dummies books?

Are you advocating macroevolution? If so, how can you be a creationist? Are you a theistic evolutionist?

P.S. I only read the first few posts at first. I see you are not advocating evolution at all. I'm not strong in math and physics, so when the math thing comes into microbiology, I kind of shy away--any suggestions or encouragement? Dummies books?

The simulation works for simple genomes. But when you get into the thousands of base pairs, the chance of a beneficial set of mutations becomes extremely slim.